1. Field of the Invention
The present invention relates to an image processing method and image processing device thereof for image alignment, and more particularly, to an image processing method and image processing device thereof capable of aligning images sequentially captured.
2. Description of the Prior Art
Mobile devices, such as digital camera, PDA and mobile phone, are commonly used in daily life. The functions of the module devices become more and more diversified. For example, the PDA or the mobile phone is usually equipped with a digital camera for capturing images. Since the intensity range of an image which is captured in single shot is limited, parts of the image may be underexposed or overexposed when the digital camera captures the image with an extensive intensity range.
In the prior art, the high dynamic range (HDR) image technology is utilized to extend limited intensity range. The high dynamic range image technology sequentially captures images with different intensity ranges and combines the captured images according to appropriate coefficients, so as to acquire a high quality image. However, if the image capture device (i.e. digital camera) is not equipped with tripod while sequentially capturing the images with different intensity ranges, there would be slight shifts between the images. In such a condition, the detail of the combined image would become fuzzy and the quality of image would be decreased. Thus, the images need to be aligned before combining the images sequentially captured.
The most common method of correcting the displacements between the images sequentially captured is scale-invariant feature transform (SIFT). Please refer to FIG. 1, which is a schematic diagram of the scale-invariant feature transform. The basic concept of the scale-invariant feature transform is searching characteristic pixels under different Gaussian blur levels. The characteristic pixels are the pixel with the maximum difference of Gaussian within a part of the image. When a first characteristic pixel not only has the maximum difference of Gaussian in the octave thereof but also has the maximum difference of Gaussian in the upper octave and the lower octave, the first characteristic pixel is sufficiently representative. Via comparing the representative characteristic pixels of the images sequentially captured, the images can be correctly aligned. The accuracy of the scale-invariant feature transform is considerably high. However, the steps of building multi-layers of Gaussian blur for searching the characteristic pixels are time-consuming. As can be seen from the above, the prior art is needed to be improved.